Everything You Need to Know About Conversational AI

We encounter conversational AI so often these days that we don’t even tend to notice it. Whenever we playfully ask Siri to tell us a joke or when we talk to especially smart chatbots in an online store, these are all examples of machines being able to respond to humans in an accurate manner. And just a few months ago (in August 2022), Google presented a robot that was able to understand spoken commands and translate them into a sequence of physical actions. 

How impressive does it sound?

With Artificial Intelligence evolving at a rapid pace, it’s quite interesting to look at where it all started and where it is heading in the future. Thus, let’s review what conversational AI is, how it differs from chatbots, and what we can expect from it in the near future.

Everything You Need to Know About Conversational AI

What is conversational AI and how does it differ from chatbots?

While there is no official and universal definition, we can say that conversational AI is a set of technologies that enable machines to communicate with people by understanding and processing human inputs in various formats. In simple words, conversational AI is a type of artificial intelligence that helps machines understand human language and respond correspondingly to it.

Now, when we say conversational AI, the first thing that comes to mind is a chatbot. Some people may say it’s the same but it’s not. So what is a key differentiator of conversational AI?

Here is the thing:

An AI-powered chatbot is built on the base of a conversational AI platform but it’s just one example of conversational AI. There are also virtual assistants, automated messaging systems, and agent-assisting bots – and all of them belong to conversational AI.

However, not all chatbots belong to conversational AI. If a chatbot is human-scripted or rule-based, it will be just an ordinary chatbot without any AI involved in its design. Hence, the main thing to remember is that conversational AI always implies the use of artificial intelligence when designing a smart virtual assistant – and there can be virtual assistants without any AI under the hood.

The main benefits of conversational AI 

Companies that use conversational AI in their processes include such big names as Spotify, Wall Street Journal, The BBC, Domino’s Pizza, and Lufthansa. So there must be a really good reason for these brands to invest in conversational AI technology, right?

The biggest benefits that conversational AI brings to a business are:

  • Lower costs of customer service: the cost of implementing an AI-powered virtual assistant is lower than hiring and training employees to perform the same tasks.
  • Better customer experience: due to the ability of AI to immediately provide requested information, the use of this technology increases user satisfaction and boosts user experience greatly.
  • User data collection: in addition to assisting users, a smart AI bot can also collect data about their preferences, interests, online behavior, and much more. Needless to say how important such data is for one’s marketing strategies. 
  • Better scalability: as the number of customers grows, it might be hard for human agents to keep up with their requests. However, it’s not an issue for AI since the efficiency of the technology does not depend on the number of requests it receives.

How do I implement a conversational AI chatbot into my processes?

The easiest way to add a smart virtual assistant to your processes is by using a readymade conversational AI platform. Examples of conversational AI companies are IBM Watson Assistant, Cognigy, Senseforth.ai, Amazon Lex, and many others. The best thing about such solutions is that you don’t have to do anything related to programming – all you have to do is to set up the bot according to your needs.

However, there is also an option for custom development in case you have a specific request or want to have a one-of-a-kind conversational AI chatbot. In this case, we recommend reaching out to a software vendor with experience in machine learning so they can offer the best solution for you,

Phew, that was a bit complex but we did it. And now let’s rewind a bit and look at the history of conversational AI and where it all started.

A brief history of conversational AI

A starting point in the history of conversational AI is ELIZA – an early NLP computer program that was developed between 1964-1966 by Joseph Weizenbaum. ELIZA was among the first chatterbots to attempt the Turing test and was designed to parody “the responses of a non-directional psychotherapist in an initial psychiatric interview” and to show that “the communication between man and machine was superficial”. ELIZA was realizing the technique of “active listening” which is widely used in psychological sessions. The technique implies using such methods as making pauses, repeating the question back to the patient, or paraphrasing said words. So when someone told ELIZA “I have a headache”, the machine could answer something like “Why do you have a headache?”. In this way, ELIZA kind of kept up with the conversation but did not understand the context nor provided any useful information in response to the input.

What came next?

As technology was evolving, the next big milestone was the appearance of mobile virtual assistants like Siri and Cortana. Even though there were other, simpler mobile chatbots that provided information on weather or news before, it was Siri that took a step forward and became something bigger and more personalized than an ordinary bot. And with the advancement of such virtual assistants, artificial intelligence became more available and widespread.

Today, the main focus of researchers lies within the personalization of artificial intelligence and attempts to make computers think in a human-like way. The best example that we can provide here is probably Pathways AI by Google. This is a revolutionary AI architecture that strives to make a single AI model capable of multitasking and of perceiving information through multiple senses (just like we do). And Google is already making great strides in Pathways AI development – we’ll talk more about it below. 


Let’s look at how conversational AI works.

The main components of conversational AI and the way it works

On the surface, it seems pretty simple: we enter our request into a machine (i.e. a smartphone) and the chatbot provides us with an answer. What we don’t see is what’s happening inside its little machine brain and how exactly the bot processes our input. So the full process would go in the following way:

  • A user puts an input (in a text or voice format) in the machine;
  • The machine processes the request (NLP);
  • The machine understands the request and user’s intent (NLU);
  • The machine responds back.

Now we need to decipher the abbreviations above. NLP stands for natural language processing and NLU means natural language understanding. When a machine receives a request, it uses NLP to correct spelling, interpret grammar (i.e. the tenses used in a sentence), and recognize the sentiment. As well, NLP breaks the request down into words and sentences (in case it’s a long request) to make it easier for the machine to understand what the user needs.

Once the request is processed by NLP, the machine will use natural language understanding to actually understand the request. 

What do we mean, by “understand”?

That means a machine will determine whether the message is positive, neutral, or negative, and what intent it has. This is possible due to Deep Learning and Machine Learning technologies that work together to break down a user’s request into components that can be understood by a machine. As a result of all this processing, a machine then forms a corresponding response to a user’s input with the help of the natural language generation (NLG) technique. And don’t forget that due to ML and deep learning, conversational AI keeps evolving and learning so the more inputs a machine is getting, the smarter it becomes.

As for the main components of conversational AI, they are:

  • Machine learning
  • Natural language processing
  • Computer vision
  • Speech recognition
  • Text analysis 

In this way, conversational AI can recognize different forms of inputs correctly and provide a very human-like interaction with users.

But it gets better.

The future of conversational AI

Even though it may seem like conversational AI is already a massive tech wonder, there is still room for improvement. And while different sources list various trends for the future of conversational AI, all of them kind of fall under the category of AI becoming smarter and more proactive. In this way, companies strive to provide better user experience, better personalization, and better services by deploying smart AI-powered assistants and using them across departments. 

But what’s meant by AI becoming smarter and more proactive? 

First, researchers hope to enable AI to predict your possible requests and provide the best responses to them before you even make an input. And second, AI is becoming more and more human-like in terms of “thinking” – remember we talked about PaLM briefly? Now it’s time to elaborate a bit more on the topic.

So PaLM is all about enabling robots to multitask – now Google is taking it a step further. In August 2022, Google held a demonstration in its robotics lab. A researcher wrote an “I’m hungry” command on a laptop connected to a robot and the robot immediately reacted by picking up a bag of chips and offering them to the researcher.

Want to know the best part?

What’s so amazing about it is the fact that the robot was not programmed in advance to respond to the researcher’s request in the way it did. Instead, it really understood the request and that was achieved by the robot previously scraping millions of pages of text on the web. That means, people won’t have to use specific wording when addressing robots – instead, they can say “I’m tired” and a robot may offer them a pillow. 

This is a real breakthrough in robotics and conversational AI as this brings robots closer to human-like natural interactions. So we can definitely say that the future will bring us intelligent AI assistants that may even be capable of making recommendations and disagreeing with us. 

Final thoughts

Conversational AI is a great asset that empowers companies with outstanding customer service and provides an excellent experience to users. It is exciting to watch its development and it’s even more exciting to see industries adopting conversational AI and thus adjusting to the ever-changing requirements of a modern customer. 


What is a conversational AI platform?

It is a platform that enables you to implement a conversational AI solution in your processes. In other words, you can build a conversational interface for the apps by using text and voice. Thus, you can use smart AI-powered bots to better serve your customers and analyze their needs and online behavior. 

What is a key differentiator of conversational AI?

The key difference between conversational AI and a chatbot, for example, is the use of artificial intelligence technology in conversational AI. Another big differentiator is that a conversational AI solution is able to keep up a human-like conversation by understanding the context of the users’ inputs.

What is an example of conversational ai?

If speaking about a general example, it would be a smart chatbot or a virtual assistant (like Siri). But if we talk about specific examples, that would be IBM Watson, Cognigy, Senseforth.ai, Amazon Lex, and many others. As for the use cases, conversational AI is capable of scheduling appointments, executing financial transactions, or recommending products and services.

How to deploy conversational AI?

You have two options here – either to design a custom AI solution or to use a readymade one (see the examples above). If you go with the first option, you’ll need to find a reliable software vendor who will take full care of developing and implementing conversational AI in your processes. If you choose the second option, you’ll need to deploy the solution in accordance with the user guide.

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